Reinforcement Learning (RL) has achieved numerous breakthroughs [1]}, [2]}, [3]} in recent years both for single- and multi-agent environments. However, sample efficiency has always been a prominent issue as a tremendous number of interactions is required by Model-Free Reinforcement Learning (MFRL) approaches to achieve optimal performance. Model-Based Reinforcement Learning (MBRL) tries to alleviate this issue by allowing the agent to interact with a learned model of the environment instead. It has shown success both in environments with known world dynamics such as shogi or go [4]} and where dynamics need to be learned from samples such as Atari [5]}, [6]}. However, translating this success to multi-agent settings is non-trivial. Several studies [7]}, [8]} have shown that MBRL approaches can be much more sample efficient than their MFRL counterparts in environments with two agents. Nonetheless, all of the current state-of-the-art MFRL approaches on popular multi-agent benchmarks such as StarCraft Multi-Agent Challenge [9]} remain uncontested.
